用贝叶斯框架融合多源数据，提升无标签目标域的识别准确率
Hierarchical Bayesian Framework for Multisource Domain Adaptation
- 基于层级贝叶斯建模，利用多源域分布相似性优化预训练
- 在Daily-DA RGB数据集上相比最优方法提升17.29%准确率
- 适合处理多源域适应中的模型泛化与标签缺失问题
多源域自适应（MDA）旨在利用多个带标签的源数据集来推断无标签目标数据集的标签。现有方法多为临时方案，源模型预训练或采用权重共享，或独立训练。本文提出一种贝叶斯框架用于MDA的预训练，考虑不同源域分布通常相似这一特性。层级贝叶斯框架通过利用不同源数据分布之间的相似性，优化了MDA的预训练过程。实验表明，该框架在大规模基准数据集上的识别任务中显著提升准确率。在具有挑战性的多域基准每日动作识别（Daily-DA RGB视频）任务中，相较于文献中先进方法，本框架实现了17.29%的准确率提升。
原文摘要 · Abstract (English)
Multisource domain adaptation (MDA) aims to use multiple source datasets with available labels to infer labels on a target dataset without available labels for target supervision. Prior works on MDA in the literature is ad-hoc as the pretraining of source models is either based on weight sharing or uses independently trained models. This work proposes a Bayesian framework for pretraining in MDA by considering that the distributions of different source domains are typically similar. The Hierarchical Bayesian Framework uses similarity between the different source data distributions to optimize the pretraining for MDA. Experiments using the proposed Bayesian framework for MDA show that our framework improves accuracy on recognition tasks for a large benchmark dataset. Performance comparison with state-of-the-art MDA methods on the challenging problem of human action recognition in multi-domain benchmark Daily-DA RGB video shows the proposed Bayesian Framework offers a 17.29% improvement in accuracy when compared to the state-of-the-art methods in the literature.
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